AI Might Change the Game—But People Still Build the Team

Several senior business leaders recently predicted that 40 to 50 percent of their employees will be replaced by AI. Staggering. First, I’m not sure that they really understand the current state of AI, and second, more importantly, they clearly don’t understand the full contribution of their people. 

The lean product and process development socio-technical model of product development is the perfect framework to bring the disparate AI technologies and people of various disciplines together.

That is sad. And what bothers me most is the way so many leaders are framing AI to their teams. Reality? Maybe, but that’s not the point. I’ll just say that this approach is the polar opposite of the “working together, people first” environment that I had the privilege of experiencing first-hand at Ford that then-CEO Alan Mulally led with every day. I saw the difference it made, and I can only hope that other leaders out there are being just as transparent while modeling Alan’s thoughtful, creative, and inspiring approach to people. 

AI in product development 

Technology has enabled product development in one form or another for a long time.  Smart CAD, parametric solid models, simulation programs, VR, virtual builds, white and blue light scanning, and many other tools have changed the engineering landscape since I first learned orthographic projection and manual section cutting on the drafting board.   

But I sense that AI will be bigger. Much bigger. I suspect it will dramatically impact nearly every aspect of how we develop new products. It will change the way we understand our customers, generate concepts, design, test, procure materials, launch, and manufacture even our most complex products. AI will be embedded both in our products and the way we develop them. 

At this point, the people I work with and talk to are still experimenting their way forward, largely using AI to enhance existing technologies like design, simulation, and coding. But these companies have begun to do some interesting experiments in early concept refinement, testing, and design optimization. They are improving project management and creating greater transparency by capturing meetings and sharing with the broader (often distributed) team.  

Lean Product and Process Development: perfect framework for integrating AI and people 

So, what is the future of lean product and process development in this new world? AI is and will continue to be a powerful tool to improve development capability.  

More importantly, lean product and process development provides the ideal structure for applying AI. As we know, lean product and process development principles and practices enhance collaboration, improve communication, enable cross-functional workstream flow, and allow us to create amazing new value streams. 

In fact, the  lean product and process development (LPPD) socio-technical model of product development is the perfect framework to bring the disparate AI technologies and people of various disciplines together.   

The principles Toyota shared with my co-author Jeff Liker and me over 20 years ago are worth seriously considering again in this totally new context.

A company I worked with recently is improving its set-based engineering utilizing digital twin and AI-enhanced simulation technology to enable them to experiment with thousands of subtle design variations and rank performance based on multiple criteria, including manufacturing with some success. 

Another is leveraging AI to better understand its value stream. More specifically, they are looking at various use cases and associated repairs to improve both maintenance requirements and serviceability.   

Still another is beginning to use AI to analyze volumes of field quality data, perform complex system analysis, and aid in both immediate issue closure and design in prevent reoccurrence countermeasures.  

There is likely much more we can do with AI and LPPD. Helping us to analyze product and process development performance is a start. Perhaps we can eliminate massively wasteful executive gate reviews with AI? Or create an even better hardware–software development flow?  

I can imagine AI aiding in analysis of complex system interactions, smarter common component use and platform strategies, creating better BoD and BoM alignment based on product performance and process capability data, improving design reviews, and finally, helping to create more environmentally sustainable product life cycles.  

I could go on as I am sure you could. But what might be more useful is to suggest some principles to guide our experimentation with AI in LPPD. The principles Toyota shared with my co-author Jeff Liker and me over 20 years ago are worth seriously considering again in this totally new context: 

1. Seamlessly integrate technology into the development system – don’t treat it as a “bolt-on”.  
2. Customize the technology to fit your needs.  Take the time to truly understand its implications. 
3. Start with right-sized tech, not king-sized. 
4. Use it to enhance people’s performance, not to eliminate them. Technology should be in the service of people, not people in the service of technology.  
5. Implement tech to improve your ability to deliver new value to your customer, not for its own sake. 

Not perfect, and certainly not comprehensive, but not a bad place to start either.